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Mathematical Problems in Engineering
Volume 2014, Article ID 716571, 9 pages
http://dx.doi.org/10.1155/2014/716571
Research Article

Forecasting Crude Oil Price with Multiscale Denoising Ensemble Model

1School of Earth Science and Resources, Chang’an University, Xi’an 710054, China
2School of Economics and Management, Beijing University of Chemical Technology, Beijing 100029, China
3International Business School, Shaanxi Normal University, Xi’an 710062, China
4Department of Management Sciences, City University of Hong Kong, Tat Chee Avenue, Kowloon Tong, Kowloon, Hong Kong
5College of Information Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China

Received 21 February 2014; Accepted 6 April 2014; Published 8 May 2014

Academic Editor: Pankaj Gupta

Copyright © 2014 Xia Li et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Crude oil price becomes more volatile and sensitive to increasingly diversified influencing factors with higher level of deregulations worldwide. Current methodologies are being challenged as they have been constrained by traditional approaches assuming homogeneous time horizons and investment strategies. Approximations they provided over the long term time horizon no longer satisfy the accuracy requirement at shorter term and more microlevels. This paper proposes a novel crude oil price forecasting model based on the wavelet denoising ARMA models ensemble by least square support vector regression with the reduced forecasting matrix dimensions by independent component analysis. The proposed methodology combines the multi resolution analysis and nonlinear ensemble framework. The wavelet denoising based algorithm is introduced to separate and extract the underlying data components with distinct features, corresponding to investors with different investment scales, which are modeled with time series models of different specifications and parameters. Then least square support vector regression is introduced to nonlinearly ensemble results based on different wavelet families to further reduce the estimation biases and improve the forecasting generalizability. Empirical studies show the significant performance improvement when the proposed model is tested against the bench-mark models.